A statistical approach to fast nowcasting of lightning potential fields

North, Joshua S.; Stanley, Zofia; Kleiber, William; Deierling, Wiebke; Gilleland, Eric; Steiner, Matthias

doi:10.5194/ascmo-6-79-2020

Cited by 2 publications

(3 citation statements)

References 32 publications

(39 reference statements)

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“…Zhang et al (2020) presented a density-based convective storm identification method for weather radar data. North et al (2020) used the heat equation to define a redistribution kernel, and a simple linear advection scheme was shown to work well in a lightning prediction example. Yücelbaş et al (2021) used effective meteorological parameters to preestimate distance-based lightning.…”

Section: Instructionmentioning

confidence: 99%

Lightning Strike Location Identification Based on 3D Weather Radar Data

Zhang

et al. 2021

Front. Environ. Sci.

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Lightning is an instantaneous, intense, and convective weather phenomenon that can produce great destructive power and easily cause serious economic losses and casualties. It always occurs in convective storms with small spatial scales and short life cycles. Weather radar is one of the best operational instruments that can monitor the detailed 3D structures of convective storms at high spatial and temporal resolutions. Thus, extracting the features related to lightning automatically from 3D weather radar data to identify lightning strike locations would significantly benefit future lightning predictions. This article makes a bold attempt to apply three-dimensional radar data to identify lightning strike locations, thereby laying the foundation for the subsequent accurate and real-time prediction of lightning locations. First, that issue is transformed into a binary classification problem. Then, a suitable dataset for the recognition of lightning strike locations based on 3D radar data is constructed for system training and evaluation purposes. Furthermore, the machine learning methods of a convolutional neural network, logistic regression, a random forest, and k-nearest neighbors are employed to carry out experiments. The results show that the convolutional neural network has the best performance in identifying lightning strike locations. This technique is followed by the random forest and k-nearest neighbors, and the logistic regression produces the worst manifestation.

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Section: Instructionmentioning

confidence: 99%

Lightning Strike Location Identification Based on 3D Weather Radar Data

Zhang

et al. 2021

Front. Environ. Sci.

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“…(2006), Youngman and Stephenson (2016), and North et al. (2020), while the problem of modeling rainfall was tackled, for example, by Paschalis et al. (2013), Leblois and Creutin (2013), Niemi et al.…”

Section: Introduction: the Rainfall Paradigmmentioning

confidence: 99%

“…However, building models incorporating these properties is challenging. Recent attempts concerning wind speed and lightning are provided for example by Gneiting et al (2006), Youngman and Stephenson (2016), and North et al (2020), while the problem of modeling rainfall was tackled, for example, by Paschalis et al (2013), Leblois and Creutin (2013), Niemi et al (2016), Nerini et al (2017), as discussed below in more depth.…”

mentioning

confidence: 99%

Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

Papalexiou

Serinaldi

Porcu

2021

Water Resources Research

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Realistic stochastic simulation of hydro-environmental fluxes in space and time, such as rainfall, is challenging yet of paramount importance to inform environmental risk analysis and decision making under uncertainty. Here, we advance random field simulation by introducing the concepts of general velocity fields and general anisotropy transformations.This expands the capabilities of the so-called Complete Stochastic Modeling Solution (CoSMoS) framework enabling the simulation of random fields preserving: (1) any non-Gaussian marginal distributions, (2) any spatiotemporal correlation structure, (3) general advection expressed by velocity fields with locally varying speed and direction, and (4) locally varying anisotropy. We also introduce new copula-based spatiotemporal correlation structures and provide conditions guaranteeing their positive definiteness. To illustrate the potential of CoSMoS, we simulate random fields with complex patterns and motion mimicking rainfall storms moving across an area, spiraling fields resembling weather cyclones, fields converging to (or diverging from) a point, and colliding air masses. The proposed methodology is implemented in the freely available CoSMoS R package.

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A statistical approach to fast nowcasting of lightning potential fields

Cited by 2 publications

References 32 publications

Lightning Strike Location Identification Based on 3D Weather Radar Data

Lightning Strike Location Identification Based on 3D Weather Radar Data

Advancing Space‐Time Simulation of Random Fields: From Storms to Cyclones and Beyond

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